In this notebook we conduct exploratory factor analyses (EFAs) on the datasets for our studies of concepts of mental life, in which each participants judged the various mental capacities of a particular target entity. We analyze datasets for adults and children from each of our five field sites: the US, Ghana, Thailand, China, and Vanuatu.
This notebook contains the results presented in the supplement, in which we recode our three-point response scale to have two points (no = 0, kind of or yes = 1) and use tetrachoric correlations.
country n
US 127
Ghana 150
Thailand 150
China 136
Vanuatu 148
Total 711
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefullyThe estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefullyThe estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefullyThe estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 3
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 1
the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used
funs() is soft deprecated as of dplyr 0.8.0
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
[90mThis warning is displayed once per session.[39m
country n
US 117
Ghana 150
Thailand 152
China 131
Vanuatu 143
Total 693
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefullyThe estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 2 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 1
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used
See All samples, below.
the condition has length > 1 and only the first element will be usedVectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.the condition has length > 1 and only the first element will be usedVectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used
Column `country` joining character vector and factor, coercing into character vector
Column `country` joining character vector and factor, coercing into character vector
Column `country` joining character vector and factor, coercing into character vector
Column `country` joining character vector and factor, coercing into character vector
Column `country` joining character vector and factor, coercing into character vector
Column `country` joining character vector and factor, coercing into character vector